Development and application of a machine vision system for measurement of surface roughness

Abstract

Monioring of surface roughness is an essential component in planning of machining processes as it affects the surface quality and dimensional accuracy of machined components. In this study, the development and application of a machine vision system suitable for on-line measurement of surface roughness of machined components using artificial neural network (ANN) is described. The system, which was based on digital image processing of the machined surface, consisted of a CCD camera, PC, Microsoft Windows Video Maker, frame grabber, Video to USB cable, digital image processing software (Photoshop, and MATLAB digital image processing toolbox), and two light sources. The images of the machined surface were captured; analyzed and optical roughness features were estimated using the 2-D fast Fourier transform (FFT) algorithm. A multilayer perceptron (MLP) neural network was used to model and predict the optical roughness values. Tool wear index and five features extracted from the surface images were used as input dataset in training and testing the ANN model. The results showed that the ANN predicted optical roughness values were found to be in close agreement with the calculated values (R2-value = 0.9529). Thus, indicating that the proposed machine vision system and ANN model are adequate for online monitoring and control of surface roughness in machining environment.